dc.contributor.author | Cheng, X | |
dc.contributor.author | Wu, Y | |
dc.contributor.author | Min, G | |
dc.contributor.author | Zomaya, A | |
dc.contributor.author | Fang, X | |
dc.date.accessioned | 2020-03-12T11:12:09Z | |
dc.date.issued | 2020-06-03 | |
dc.description.abstract | Network slicing, as a key 5G enabling technology, is
promising to support with more flexibility, agility, and intelligence
towards the provisioned services and infrastructure management.
Fulfilling these tasks is challenging, as nowadays networks are
increasingly heterogeneous, dynamic and large-dimensioned. This
contradicts the dominant network slicing solutions that only customize immediate performance over one snapshot of the system in
the literature. Instead, this paper first presents a two-stage slicing
optimization model with time-averaged metrics to safeguard
the network slicing in the dynamical networks, where prior
environmental knowledge is absent but can be partially observed
at runtime. Directly solving an off-line solution to this problem
is intractable since the future system realizations are unknown
before decisions. Therefore, we propose a learning augmented
optimization approach with deep learning and Lyapunov stability
theories. This enables the system to learn a safe slicing solution
from both historical records and run-time observations. We prove
that the proposed solution is always feasible and nearly optimal,
up to a constant additive factor. Finally, we demonstrate up to
2.6× improvement in the simulation when compared with three
state-of-the-art algorithms. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.identifier.citation | Vol. 38 (7), pp. 1600 - 1613 | en_GB |
dc.identifier.doi | 10.1109/JSAC.2020.2999696 | |
dc.identifier.grantnumber | EP/R030863/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/120227 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2020 IEEE | |
dc.subject | Network slicing | en_GB |
dc.subject | 5G | en_GB |
dc.subject | deep learning | en_GB |
dc.subject | Lyapunov optimization | en_GB |
dc.title | Safeguard Network Slicing in 5G: A Learning Augmented Optimization Approach | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-03-12T11:12:09Z | |
dc.identifier.issn | 0733-8716 | |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record. | en_GB |
dc.identifier.journal | IEEE Journal on Selected Areas in Communications | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2020-03-02 | |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2020-03-02 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-03-11T21:19:06Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2020-06-09T15:05:01Z | |
refterms.panel | B | en_GB |